We introduce an algorithm for model-based hierarchical reinforcement learning
to acquire self-contained transition and reward models suitable for
probabilistic planning at multiple levels of abstraction. We call this
framework Planning with Abstract Learned Models (PALM). By representing
subtasks symbolically using a new formal structure, the lifted abstract Markov
decision process (L-AMDP), PALM learns models that are independent and modular.
Through our experiments, we show how PALM integrates planning and execution,
facilitating a rapid and efficient learning of abstract, hierarchical models.
We also demonstrate the increased potential for learned models to be
transferred to new and related tasks.

该研究利用一种新的形式结构，提出了一种基于模型的层次强化学习算法，名为 PALM，可学习独立、模块化的转移和奖励模型用于概率规划，并演示了其将规划和执行进行集成，以快速有效地学习抽象、分层模型以及转移至新的相关任务的增强潜力。